Deep learning-based algorithms for long-term prediction of chlorophyll-a in catchment streams

•We developed a deep learning-based framework for long-term Chl-a simulation.•The performance of six state of the art deep learning algorithms was compared.•Our study employed separate sub-basins to train and evaluate DL models.•Chl-a prediction is improved by using sub-basin characteristics as inpu...

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Vydané v:Journal of hydrology (Amsterdam) Ročník 626; s. 130240
Hlavní autori: Abbas, Ather, Park, Minji, Baek, Sang-Soo, Cho, Kyung Hwa
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.11.2023
Predmet:
chlorophyll
Chlorophyll-a
climate
Deep learning
Explainable-AI
Machine learning
meteorological data
neural networks
poisonous algae
prediction
rivers
surface water
water quality
watersheds
Deep learning
Explainable-AI
Chlorophyll-a
Machine learning
ISSN:0022-1694, 1879-2707
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Shrnutí:•We developed a deep learning-based framework for long-term Chl-a simulation.•The performance of six state of the art deep learning algorithms was compared.•Our study employed separate sub-basins to train and evaluate DL models.•Chl-a prediction is improved by using sub-basin characteristics as input data.•Attention based LSTM model can simulate and explain Chl-a behavior in surface water. Accurate estimation of harmful algal blooms is imperative for the protection of surface water. Chlorophyll-a (Chl-a) which is used as a proxy for estimating the algal concentration, is affected by a wide range of weather and physicochemical factors that act at varying spatial and temporal scales. Deep learning (DL) based models such as Long-Short Term Memory (LSTM) and Convolution Neural Networks (CNNs) have shown promising results for hydrological and Chl-a simulations. Recently several variants of LSTM and CNNs have been developed which can model highly non-linear relationships between input and target data. Therefore, these advanced DL methods have the potential for long-term simulation of Chl-a. Previous DL-based studies on Chl-a simulation have developed site-dependent models. This indicates that the DL models were trained and evaluated using data from the same site, making it difficult to apply these models to other sites. Development of site-independent models requires a more robust training strategy which can result in DL models that can be evaluated in new novel situations. To address these issues, we propose a DL-based framework which can incorporate irregularly measured water quality observations, static physical features, and climate data measured at constant time steps. In this framework, we compared the performance of six state of the art DL methods which include (1) LSTM, (2) CNN, (3) Temporal Convolution Networks (TCN), (4) CNN-LSTM, (5) LSTM based autoencoder, and (6) input-attention LSTM (IA-LSTM). The IA-LSTM is an explainable DL method which can select important hydrologic, climatic and water quality parameters for Chl-a prediction. Our results indicate that the IA-LSTM exhibited the best performance, with an R2 value of 0.85 at the training site and 0.52 at the test site. We showed that attention-based deep learning models improve the prediction performance and make the black-box deep learning models interpretable and explainable. The attention-based deep learning models indicated that Chl-a concentration in the Nakdong River was strongly affected by climate factors during the previous three days. The proposed DL framework can be adopted to develop regional water quality models using deep learning.
Bibliografia:ObjectType-Article-1
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content type line 23
ISSN:0022-1694
1879-2707
DOI:10.1016/j.jhydrol.2023.130240